Synthesis and analysis of temporal fault trees with PANDORA: The time of Priority AND gates

Classical Fault Tree Analysis (FTA) can determine the effects of combinations of failure events on a system but cannot capture the significance of the temporal order of events, which may be critical. In this paper, we propose an extension based on formal definition and use of Priority AND gates that enables representation of event sequences and analysis of temporal relationships in FTA. In addition, we show how this type of temporal analysis can be used in conjunction with a recently proposed method for automated fault tree synthesis to allow accurate failure analyses of system models to be carried out efficiently. The approach is demonstrated on a generic system with a shared backup component. The paper tentatively concludes that this type of temporal FTA can provide a more precise and ultimately more correct insight into the failure behaviour of a system.     

基于半马尔科夫过程的逻辑门可靠性分析

动态故障树分析方法是在静态故障树的基础上拓展而来的自上而下的图形化演绎技术,可以很好地对具有复杂失效行为和交互作用的系统进行建模,进而分析系统的可靠性。本文从动态故障树逻辑门的可靠性建模与分析入手,结合半马尔科夫过程原理,将动态逻辑门转化为半马尔科夫链。其次给出在半马尔科夫链中动态逻辑门输出事件的发生概率和系统可靠性的计算公式。提出各种逻辑门到半马尔科夫链的通用转化模型,通过更改通用模型中的相关参数,将逻辑门转化为半马尔科夫链。最后,基于半马尔科夫过程求解动态逻辑门输出事件的发生概率,以动态优先与门、顺序相关门和备件门为例,并给出系统可靠性的计算公式。     

Knowledge‐based scenario tree generation methods and application in multiperiod portfolio selection problem

A scenario tree is an efficient way to represent a stochastic data process in decision problems under uncertainty. This paper addresses how to efficiently generate appropriate scenario trees. A knowledge‐based scenario tree generation method is proposed; the new method is further improved by accounting for subjective judgements or expectations about the random future. Compared with existing approaches, complicated mathematical models and time‐consuming estimation, simulation and optimization problem solution are avoided in our knowledge‐based algorithms, and large‐scale scenario trees can be quickly generated. To show the advantages of the new algorithms, a multiperiod portfolio selection problem is considered, and a dynamic risk measure is adopted to control the intermediate risk, which is superior to the single‐period risk measure used in the existing literature. A series of numerical experiments are carried out by using real trading data from the Shanghai stock market. The results show that the scenarios generated by our algorithms can properly represent the underlying distribution; our algorithms have high performance, say, a scenario tree with up to 10,000 scenarios can be generated in less than a half minute. The applications in the multiperiod portfolio management problem demonstrate that our scenario tree generation methods are stable, and the optimal trading strategies obtained with the generated scenario tree are reasonable, efficient and robust. Copyright © 2013 John Wiley & Sons, Ltd.     

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

The credit scoring is a risk evaluation task considered as a critical decision for financial institutions in order to avoid wrong decision that may result in huge amount of losses. Classification models are one of the most widely used groups of data mining approaches that greatly help decision makers and managers to reduce their credit risk of granting credits to customers instead of intuitive experience or portfolio management. Accuracy is one of the most important criteria in order to choose a credit‐scoring model; and hence, the researches directed at improving upon the effectiveness of credit scoring models have never been stopped. In this article, a hybrid binary classification model, namely FMLP, is proposed for credit scoring, based on the basic concepts of fuzzy logic and artificial neural networks (ANNs). In the proposed model, instead of crisp weights and biases, used in traditional multilayer perceptrons (MLPs), fuzzy numbers are used in order to better model of the uncertainties and complexities in financial data sets. Empirical results of three well‐known benchmark credit data sets indicate that hybrid proposed model outperforms its component and also other those classification models such as support vector machines (SVMs), K‐nearest neighbor (KNN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA). Therefore, it can be concluded that the proposed model can be an appropriate alternative tool for financial binary classification problems, especially in high uncertainty conditions. © 2013 Wiley Periodicals, Inc. Complexity 18: 46–57, 2013     

Priority based fuzzy goal programming technique to fractional fuzzy goals using dynamic programming

This paper describes the use of preemptive priority based fuzzy goal programming method to fuzzy multiobjective fractional decision making problems under the framework of multistage dynamic programming. In the proposed approach, the membership functions for the defined objective goals with fuzzy aspiration levels are determined first without linearizing the fractional objectives which may have linear or nonlinear forms. Then the problem is solved recursively for achievement of the highest membership value (unity) by using priority based goal programming methodology at each decision stages and thereby identifying the optimal decision in the present decision making arena. A numerical example is solved to represent potentiality of the proposed approach.     

基于二元联系数距离的不确定语言多属性决策模型

研究了属性权重范围已知,方案主观偏好值为语言变量,决策信息为不确定语言决策矩阵的多属性决策问题.在给出不确定语言变量转换为二元联系数的公式以及二元联系数距离公式的基础上,将方案主观偏好语言评价值转换为二元联系数,将不确定语言决策矩阵转换为二元联系数决策矩阵,从而得到方案的二元联系数综合属性值,通过最小化方案的二元联系数综合属性值和主观偏好值之间距离,建立多目标优化模型,并将其转换为一个单目标规划模型计算出属性权重.然后,通过对方案的二元联系数综合属性值进行不确定性分析,得到各方案的排序总数,利用排序总数对方案进行排序择优.应用实例表明该决策方法可行有效.     

基于故障树分析的堰塞湖溃坝概率估计方法

堰塞湖排险的一个关键问题是如何针对实施不同应对措施情况下的堰塞湖溃坝概率进行估计,这是一个值得关注的重要研究课题。本文提出了一种基于故障树分析(FTA)的堰塞湖溃坝概率估计方法。首先,通过堰塞湖排险问题的实际背景分析,基于FTA构建了堰塞湖溃坝故障树的基本架构;然后,通过相关领域知识、历史案例分析、专家主观判断和多位专家主观判断信息的融合,可以确定实施某一应对措施情形下故障树中各基本事件在不同时段内发生的概率;进一步地,依据构建的故障树和基本事件发生的概率,给出了在不同时段内堰塞湖溃坝事件发生的概率的估计方法。最后,通过一个实例分析说明了本文所提出方法的可行性与有效性。     

A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k‐out‐of‐n:F systems

In this paper, a dynamic evaluation of the multistate weighted k‐out‐of‐n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi‐objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model. Copyright © 2016 John Wiley & Sons, Ltd.     

A dynamic binomial expansion technique for credit risk measurement: a Bayesian filtering approach

Credit risk measurement and management are important and current issues in the modern finance world from both the theoretical and practical perspectives. There are two major schools of thought for credit risk analysis, namely the structural models based on the asset value model originally proposed by Merton and the intensity‐based reduced form models. One of the popular credit risk models used in practice is the Binomial Expansion Technique (BET) introduced by Moody's. However, its one‐period static nature and the independence assumption for credit entities' defaults are two shortcomings for the use of BET in practical situations. Davis and Lo provided elegant ways to ease the two shortcomings of BET with their default infection and dynamic continuous‐time intensity‐based approaches. This paper first proposes a discrete‐time dynamic extension to the BET in order to incorporate the time‐dependent and time‐varying behaviour of default probabilities for measuring the risk of a credit risky portfolio. In reality, the ‘true’ default probabilities are unobservable to credit analysts and traders. Here, the uncertainties of ‘true’ default probabilities are incorporated in the context of a dynamic Bayesian paradigm. Numerical studies of the proposed model are provided.     

项目网络中考虑支配与扩散关系的组合决策

单纯侧重项目自身属性而不考虑项目关联性以及由项目衍生而来的技术、经验/信息扩散对项目组合决策时的影响,易导致决策偏差,低估具有潜在技术先导性项目的价值。对此,引用复杂网络理论,以项目关联性的视角,将项目间支配和扩散关系分别抽象为有向加权网络,运用K-shell分解方法构建项目组合网络中基于支配关系的项目影响力模型以及技术、经验/信息在项目间扩散传播的模型。然后,基于PageRank算法,综合考虑项目间支配与扩散关系,建立了项目优先级排序决策模型。最后,通过算例分析说明了该模型与算法的可行性与有效性,为企业项目组合决策提供了有益的参考。     

An adaptive algorithm in time domain for dynamic analysis of a simply supported beam subjected to a moving vehicle

This paper presents an adaptive algorithm in the time domain for the dynamic analysis of a simply supported beam subjected to the moving load and moving vehicle with/without varying surface roughness. By expanding variables at a discretized time interval, a coupled spatial‐temporal problem can be converted into a series of recursive space problems that are solved by finite element method (FEM), and a piecewised adaptive computing procedure can be carried out for different sizes of time steps. The proposed approach is numerically verified via the comparison with analytical and the Runge–Kutta method‐based solutions, and satisfactory results have been achieved. Copyright © 2011 John Wiley & Sons, Ltd.     

Constructions of Z-optimal Type-II quadriphase Z-complementary pairs

Investigating (periodic) and design sequences with good correlation properties have numerous applications in communications. Research on designing sequence pairs with good correlation properties started in the early 1950's thanks to M.J. Golay. Ideally, one of our ultimate aims in this context is to design a set of sequences whose out-of-phase auto-correlation magnitudes and cross-correlation magnitudes are very small, preferably zero. The so-called Z-complementary pair (ZCP) is one of the suitable candidates. A pair of sequences is called a Z-complementary pair (ZCP) if it has zero aperiodic autocorrelation sums (AACSs) for time-shifts within a certain region, called zero correlation zone (ZCZ). ZCPs have been widely used in different communication systems and are closely related with almost difference families, which are useful in studying partially balanced incomplete block design. Despite remarkable progress in designing ZCPs, only a few constructions of quadriphase ZCPs (QZCPs) have been reported in the literature up to now. Aiming to reducing this gap, we explore in this article several methods to design such sequences. More specifically, we propose a recursive construction based on the concatenation of sequences aimed to design Type-II QZCPs. Also, based on Turyn's construction method, we present another new Type-II QZCPs. The proposed constructions lead to Z-optimal Type-II even-length QZCPs (E-QZCPs) and Type-II odd-length QZCPs (O-QZCPs) with large ZCZ widths. Finally, we derive upper bounds for the peak-to-mean envelope-power ratio (PMEPR) of the proposed ZCPs. It turns out that our constructions lead to ZCPs with low PMEPR. These characteristics allow our QZCPs to be seen as promising for practical uses in some modern communication systems.     

基于UGC大数据挖掘的大群体两阶段风险性应急决策方法

针对在重大突发事件应急决策大数据环境下决策者偏好的不确定性及偏离群体一致性导致的风险,提出一种基于UGC大数据挖掘的大群体两阶段风险性应急决策方法。首先,通过数据挖掘和自然语言处理方法从UGC中获取公众对事件的偏好信息并构建应急决策属性体系,利用TF-IDF方法结合专家评估信息确定属性权重;其次,建立一个意见开放式的两阶段决策流程,提出依据决策者意见的可靠度和准确度量化决策风险,利用聚类方法得到相应的成员权重,并使用TOPSIS法对决策方案进行排序。最后通过天津港“8·12”重大爆炸事故的案例分析和对比验证了所提出方法的可行性和有效性。     

Deriving crisp and consistent priorities for fuzzy AHP-based multicriteria systems using non-linear constrained optimization

Fuzzy optimization models are used to derive crisp weights (priority vectors) for the fuzzy analytic hierarchy process (AHP) based multicriteria decision making systems. These optimization models deal with the imprecise judgements of decision makers by formulating the optimization problem as the system of constrained non linear equations. Firstly, a Genetic Algorithm based heuristic solution for this optimization problem is implemented in this paper. It has been found that the crisp weights derived from this solution for fuzzy-AHP system, sometimes lead to less consistent or inconsistent solutions. To deal with this problem, we have proposed a consistency based constraint for the optimization models. A decision maker can set the consistency threshold value and if the solution exists for that threshold value then crisp weights can be derived, otherwise it can be concluded that the fuzzy comparison matrix for AHP is not consistent for the given threshold. Three examples are considered to demonstrate the effectiveness of the proposed method. Results with the proposed constraint based fuzzy optimization model are more consistent than the existing optimization models.     

一种新型风险型多指标决策方法研究

传统的风险型多指标决策模型没有考虑决策者对风险的态度,而决策者对风险的态度会影响决策的结果,针对这一问题文章在累积前景理论与灰色关联方法的基础上,提出一种考虑决策者风险偏好的风险型多指标决策的方法.该方法首先利用极差化法对风险决策矩阵进行规范化处理,并在此基础上构造出最优与最劣方案;然后利用累积前景理论与灰色关联方法构建前景值函数,并给出利用灰色关联思想确定指标权重的方法与步骤;最终求出各个方案的综合前景值并进行排序选优.通过某电信运营商对管道资源建设方案选择的实例分析说明了方法的可行性与有效性.     

考虑决策者心理行为的直觉语言多属性决策方法

针对应用直觉语言集来表达决策信息的语言多属性决策问题,在考虑决策者有限理性的心理行为基础上,提出一种决策方法。该方法通过比较每个属性下方案之间的得分函数和精确函数, 构建方案的收益-损失分析矩阵。在考虑决策者参照依赖和损失规避心理行为基础上,计算每个方案相对于其它方案在每个属性下的收益-损失值优先度;在此基础上,计算备选方案的综合优先度, 并根据其大小对方案进行排序择优。最后通过一个算例验证所提出方法的有效性和合理性。     

二层次消费与投资决策优化动态规划模型

在动态多阶段情形,投资者面临的环境不仅只有投资环境,还包括消费环境.投资者关于投资与消费的决策具有层次性.因为消费事关人的生存需要,是优先要考虑的问题,且投资的最终目的还是为了消费,所以使消费最大化应是高一层次的目标,而使投资最大化则应是次一级的目标.因此,试图建立一个二层次消费与投资决策优化动态规划模型,以便更好地模拟现实世界的情况.讨论了该模型的动态决策过程和最优解的性质.     

模糊广义判断矩阵的一致性检验及合成排序

决策评价过程中往往包含诸多不确定性、随机性和模糊性,广义判断下的ＡＨＰ－ＧＪＡＨＰ是一种广义ＡＨＰ,Ｆｕｚｚｙ 环境下的ＧＪＡＨＰ决策方法是应用集值统计的方法,在区间判断标度基础上确定模糊判断矩阵元素的正模糊数表示,并根据模糊集理论的扩展原理,求得Ｆｕｚｚｙ 环境下的模糊排序权值向量。本文给出模糊广义判断矩阵的一致性定义,讨论了各类判断形式条件下的一致性检验法与Ｆｕｚｚｙ 环境下递阶层次结构中的合成排序问题     

Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance

The primary objective in the discrimination problem is to assign a set of alternatives into predefined classes. During the last two decades several new approaches, such as mathematical programming, neural networks, machine learning, rough sets, multi-criteria decision aid (MCDA), etc., have been proposed to overcome the shortcomings of traditional, statistical and econometric techniques that have dominated this field since the 1930s. This paper focuses on the MCDA approach. A new method to achieve multi-group discrimination based on an iterative binary segmentation procedure is proposed. Five real world applications from the field of finance (credit cards assessment, country risk evaluation, credit risk assessment, corporate acquisitions, business failure prediction) are used to illustrate the efficiency of the proposed method as opposed to discriminant analysis.     

一种基于决策矩阵的DST-AHP多属性决策方法

针对层次分析法决策时存在两两判断、一致性检验次数过多和判断矩阵残缺性等问题,本文提出了一种基于决策矩阵的DST-AHP多属性决策方法。该方法结合决策矩阵的特征值,构建DST-AHP方法层次结构模型和判断矩阵,并根据判断矩阵定义不同属性下各焦元的基本概率分配函数;然后利用Dempster合成法则对基本概率分配函数值进行合成,依据合成后值对方案进行排序。最后对AHP和DST-AHP两种方法进行比较分析,说明该方法的有效性。